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Record W3106334259 · doi:10.24043/isj.133

In spaces in between–From recollections to nostalgia: Discourses of bridge and island place

2020· article· en· W3106334259 on OpenAlexvenueno aff
Jana Raadik Cottrell, Stuart Cottrell

Bibliographic record

VenueIsland Studies Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicArctic and Russian Policy Studies
Canadian institutionsnot available
Fundersnot available
KeywordsRhetoricMainlandRhetorical questionContext (archaeology)PoliticsBridge (graph theory)SociologyMainland ChinaIdentity (music)AestheticsHistoryMedia studiesGeographyPolitical scienceArchaeologyLawArtLiteratureChinaLinguistics

Abstract

fetched live from OpenAlex

Creation of a terrestrial connection to the mainland from Saaremaa Island (Estonia) has been discussed among politicians, scientists and the general public for the last decade. A fixed link has been a dream, hope, and fear in a situation where the island faces enormous societal changes in a rapidly developing young capitalist country. Islanders and visitors feel threats to their home place with or without the bridge. This paper explores public discourse of textualized landscapes as context-dependent multiple realities. Questions related to the perceptions of change of material landscapes as well as symbolic meanings of lived environment in the transition and rhetoric of everyday spatial practices are examined. The rhetorical ‘journey’ of a planned terrestrial fixed link (a bridge from an island to the mainland) is followed. Materials from an online public forum from five years related to the topic and approximately 120 online articles with more than 1800 comments from the general public were examined to reveal major themes of discourse on island place, landscape of identity as well as possible transformations of related concern. Idealized landscapes of a nostalgic past are voiced equally yet differently among political powers, islanders themselves and tourists.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.906

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.066
GPT teacher head0.378
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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